DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim objections regarding to claims 8 and 9 is withdrawn.
Claim rejections related to 35 USC § 101 regarding to claim 24 is withdrawn.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-22, 25 are rejected under 35 U.S.C. 101
because the claimed invention is directed to an abstract idea without significantly
more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be
determined whether the claim is directed to one of the four statutory categories of
invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the
claim does fall within one of the statutory categories, the second step in the analysis is
to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A
analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined
whether or not the claims recite a judicial exception (e.g., mathematical concepts,
mental processes, certain methods of organizing human activity). If it is determined in
Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the
second prong (Step 2A, Prong 2), where it is determined whether or not the claims
integrate the judicial exception into a practical application. If it is determined at step 2A,
Prong 2 that the claims do not integrate the judicial exception into a practical
application, the analysis proceeds to determining whether the claim is a patent-eligible
application of the exception (Step 2B). If an abstract idea is present in the claim, any
element or combination of elements in the claim must be sufficient to ensure that the
claim integrates the judicial exception into a practical application, or else amounts to
significantly more than the abstract idea itself. Applicant is advised to consult the 2019
PEG for more details of the analysis.
Step 1
According to the first part of the analysis, in the instant case, claims 1-14 of a method, 15, 16 of a coordinating entity, 17-22 of a method, 25 of a computer program product (dependent claim of claim 1) are of handling ML model training. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter). Step 2A,
Step 2A, Prong 1
Following the determination of whether or not the claims fall within one of the four
categories (Step 1), it must be determined if the claims recite a judicial exception (e.g.
mathematical concepts, mental processes, certain methods of organizing human
activity) (Step 2A, Prong 1). In this case, the claims are determined to recite a judicial
exception as explained below.
Regarding Claims 1, 15-17 and these claims recite
Claim 1, 15-16: in response to receiving a request to train the machine learning model: selecting, from a plurality of network nodes, a first network node to train the machine learning model based on information indicative of a performance of each of the plurality of network nodes, the information indicative of the performance of each of the plurality of network nodes comprising information indicative of a past performance of each of the plurality of network nodes, the information indicative of the past performance of each of the plurality of network nodes comprising a measure of a past effectiveness of each of the plurality of network nodes in training machine learning models; and initiating transmission of the machine learning model towards the first network node for the first network node to train the machine learning model.
Claim 17: the method as claimed in claim1; and a method performed by the first network node comprising: in response to receiving the machine learning model from the coordinating entity: training the machine learning model using training data that is available to the first network node.
The claims recite a mental process. As set forth in MPEP 2106.04(a)(2)(III)(C), “Claims can recite a mental process even if they are claimed as being performed on a computer”. These are recited at a high level such that they are disclosed as a human user performing these functions, simply using a computer as a tool-see spec, page 10, line 5-page 12, line 12, Fig. 2. Thus, the claim recites abstract ideas.
Step 2A, Prong 2
Following the determination that the claims recite a judicial exception, it must be
determined if the claims recite additional elements that integrate the exception into a
practical application of the exception (Step 2A, Prong 2). In this case, after considering
all claim elements individually and as an ordered combination, it is determined that the
claims do not include additional elements that integrate the exception into a practical
application of the exception as explained below.
In Prong Two, a claim is evaluated as a whole to determine whether the recited judicial exception is integrated into a practical application of that exception. A claim is not “directed to” a judicial exception, and thus is patent eligible, if the claim as a whole integrates the recited judicial exception into a practical application of that exception. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. MPEP 2106.04(d). The claims recite an abstract idea and further the claims as a whole does not integrate the recited judicial exception into a practical application of the exception. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. MPEP 2106.04(d).
Regarding Claims 1, 15-17 these claims
This limitation recites using one or more neural networks as a tool to perform an abstract idea, which is not indicative of integration into a practical application. MPEP 2106.05(f).)
This limitation is understood to be generic computer equipment and mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.0S(f))
MPEP § 2106.05(f): Mere Instructions to Apply an Exception. Do the additional element(s) amount to merely the words “apply it” (or an equivalent)
or are mere instructions to implement an abstract idea or other exception on a computer? (Yes)
Step 2B
Based on the determination in Step 2A of the analysis that the claims are
directed to a judicial exception, it must be determined if the claims contain any element
or combination of elements sufficient to ensure that the claim amounts to significantly
more than the judicial exception (Step 2B). In this case, after considering all claim
elements individually and as an ordered combination, it is determined that the claims do
not include additional elements that are sufficient to amount to significantly more than
the judicial exception for the same reasons given above in the Step 2A, Prong 2
analysis. Furthermore, each additional element identified above as being insignificant
extra-solution activity is also well-known, routine, conventional as described below.
Claims 1, 15-17: The claims do not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than generic computing components and field of use/technological environment which do not amount to significantly more than the abstract idea. The underlying concept merely receives information, analyzes it, and store the results of the analysis – this concept is not meaningfully different than concepts found by the courts to be abstract (see Electric Power Group, collecting information, analyzing it, and displaying certain results of the collection and analysis; see Cybersource, obtaining and comparing intangible data; see Digitech, organizing information through mathematical correlations; see Grams, diagnosing an abnormal condition by performing clinical tests and thinking about the results; see Cyberfone, using categories to organize store and transmit information; see Smartgene, comparing new and stored information and using rules to identify options). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as a combination do not amount to significantly more than the abstract idea. For example, claim 1, 15-16 recites in response to receiving a request to train the machine learning model: selecting, from a plurality of network nodes, a first network node to train the machine learning model based on information indicative of a performance of each of the plurality of network nodes, the information indicative of the performance of each of the plurality of network nodes comprising information indicative of a past performance of each of the plurality of network nodes, the information indicative of the past performance of each of the plurality of network nodes comprising a measure of a past effectiveness of each of the plurality of network nodes in training machine learning models; and initiating transmission of the machine learning model towards the first network node for the first network node to train the machine learning model and claim 17 further recite in response to receiving the machine learning model from the coordinating entity: training the machine learning model using training data that is available to the first network node These elements are recited at a high level of generality and are well-understood, routine, and conventional activities in the computer art. Generic computers performing generic computer functions, without an inventive concept, do not amount to significantly more than the abstract idea. Looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims do not amount to significantly more than the abstract idea itself.
Step 2A/2B Prong 2 Dependent Claims
Regarding to claim 2
Claim 2 merely recite other additional elements that defining the ML model which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 3-4
Claim 3-4 merely recite other additional elements that checking the ML model performance which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 5
Claim 5 merely recite other additional elements that updating an index for the network node which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 6
Claim 6 merely recite other additional elements that determining whether to add training data which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 7
Claim 7 merely recite other additional elements that initiating transmitting the training data which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 8
Claim 8 merely recite other additional elements that define the ML model transmission from the first node to the second node which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 9
Claim 9 merely recite other additional elements that defining further training a second network node and transmit the ML model from the first network node to the second network node which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 10
Claim 10 merely recite other additional elements that select the second node which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 11
Claim 11 merely recite other additional elements that select at least another node which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 12-14
Claim 12-14 merely recite other additional elements that define the information which performs generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 18
Claim 18 merely recite other additional elements that training the ML which performs generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 19-21
Claim 19-21 merely recite other additional elements that define the ML transmission which performs generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 22
Claim 22 merely recite other additional elements that identify the training data which performs generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 25
Claim 25 merely recite other additional elements that define a computer program product according to claim 1, which performs generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 8, 10-11, 15-17, 22, 25 are rejected under 35 U.S.C. 103 as being unpatentable over Qiu et al. (Qiu) US 20210133555 in view of Johnson et al. (Johnson) US 2021/0089887 and Jeuk et al. (Jeuk) US 2021/0392049
In regard to claim 1, Qiu disclose A method for handling training of a machine learning model, the method performed by a coordinating entity that is operable to coordinate the training of the machine learning model at one or more network nodes and the method comprising: (Fig. 4, 7, and 14 [0029]-[0024][0059-[0062] [0122]- [0127] learning of a ML model and a task schedular to schedule to train the ML model at worker nodes)
in response to receiving a request to train the machine learning model: (Fig. 14, [0059]-[0063] [0122]-[0127] receive the worker node ask the task scheduler to work (train) the ML model)
selecting, from a plurality of network nodes, a first network node to train the machine learning model based on the information, (Fig. 11B, 14, [0059]-[0063] [0114]-[0116] [0120]-[0127], claim 4-5, selecting a specific number of worker node (worker 1, for example) form the worker group to training the ML model based on the worker nodes are idle or not performing a ML task which represent the information. Note: please further define how to select the node to train based on what performance data with what kind of criteria to help move forward the prosecution, etc. call to discuss if necessary.)
and transmission of the machine learning model towards the first network node for the first network node to train the machine learning model. ([0051]-[0059] [0122]-[0127] the worker pull the updated partial model parameter from the server)
But Qiu fail to explicitly disclose “and initiating transmission of the machine towards the first network node learning model.”
Johnson disclose and initiating transmission of the machine learning model towards the first network node. ([0027][0055]-[0058] [0066]-[0067] transmit the initial ML model to the worker)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Johnson‘s ML model training into Qiu’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Johnson’s ML training with transmission of the ML to worker nodes would help to provide model delivery method into Qiu’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing model delivery method by transmitting the model to the worker nodes would facilitate ML training.
But Qiu and Johnson fail to explicitly disclose “train the machine learning model based on information indicative of a performance of each of the plurality of network nodes, the information indicative of the performance of each of the plurality of network nodes comprising information indicative of a past performance of each of the plurality of network nodes, the information indicative of the past performance of each of the plurality of network nodes comprising a measure of a past effectiveness of each of the plurality of network nodes in training machine learning models;”
Jeuk disclose train the machine learning model based on information indicative of a performance of each of the plurality of network nodes, the information indicative of the performance of each of the plurality of network nodes comprising information indicative of a past performance of each of the plurality of network nodes, ([0049]-[0053][0066] [0066]-[0069] train the ML model based on the operational data such as current/recent performance for any of the individual nodes within the network topology)
the information indicative of the past performance of each of the plurality of network nodes comprising a measure of a past effectiveness of each of the plurality of network nodes in training machine learning models; ([0019][0045] [0049]-[0053][0066] [0066]-[0069] current/recent performance for any of the individual nodes within the network topology include metric of the performance, etc. such as number and type of system errors, ranking, network usage, overall system performance, etc.)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Jeuk’s ML model training into Johnson and Qiu’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Jeuk’s ML model training based on the past performance of nodes would help to provide more model training criteria into Johnson and Qiu’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing model training criteria based on the past performance data would facilitate ML training.
In regard to claim 2, Qiu and Johnson, Jeuk disclose A method as claimed in claim 1,
Qiu disclose wherein: the machine learning model is: a previously untrained machine learning model; or a machine learning model previously trained by another network node of the plurality of network nodes. ([0097][0111] the model is trained at different workers)
In regard to claim 8, Qiu, Johnson, Jeuk disclose A method as claimed in claim 1,
Qiu disclose the method comprising: in response to the first network node completing the training of the machine learning model, or in response to a failure of the first network node (10) to train the machine learning model: ([0105]-[-0109][0114]-[0116] the worker complete the training task)
selecting, from the plurality of network nodes, a second network node to further train the trained machine learning model based on information indicative of a performance of each of the plurality of network nodes and/or information indicative of a quality of a network connection to each of the plurality of network nodes, wherein the first network node and the second network node are different network nodes; (Fig. 11B, 14, [0059]-[0063][0103]-[0116] [0120]-[0127], claim 4-5, selecting another worker node (worker 2, for example) form the worker group to training the ML model based on the worker nodes are idle or not performing a ML task) and
initiating transmission of a request towards the second network node to trigger a transfer of the trained machine learning model from the first network node to the second network node for the second network node to further train the machine learning model. ([0103]-[0116] communicate the model parameter with each of the other workers with the communication protocols in the worker group and for the other workers to train the model)
In regard to claim 10, Qiu, Johnson, Jeuk disclose A method as claimed in claim 8,
Qiu disclose wherein: selecting the second network node is in response to receiving the trained machine learning model from the first network node. (Fig. 11B, 14, [0059]-[0063][0103]-[0116] [0120]-[0127], claim 4-5, selecting another worker node (worker 2, for example) form the worker group to training the ML model when the model parameter are communicated with each of the other workers in the worker group and for the other workers to train the model)
In regard to claim 11, Qiu, Johnson, Jeuk disclose A method as claimed in claim 8,
Qiu disclose wherein: the method is repeated in respect of at least one other different network node (30) of the plurality of network nodes. (Fig. 11B, 14, [0039] [0054]-[0063][0103]-[0116] [0120]-[0127], claim 4-5, the process is repeated, selecting another worker node (worker 2, for example) form the worker group to training the ML model when the model parameter are communicated with each of the other workers in the worker group and for the other workers to train the model)
In regard to claim 15, claim 15 disclose A coordinating entity claim (Fig. 4, 410, [0051]-[0055] [0061]-[0063] [116]-[0127] task scheduler and server ) corresponding to the method claim 1 above and, therefore, is rejected for the same reasons set forth in the rejections of claim 1.
In regard to claim 16, claim 16 disclose A coordinating entity claim (Fig. 4, 410, [0051]-[0055] [0061]-[0063] [116]-[0127] task scheduler and server ) corresponding to the method claim 1 above and, therefore, is rejected for the same reasons set forth in the rejections of claim 1.
In regard to claim 17, Qiu disclose A method for handling training of machine learning model, the method performed by a system comprising a plurality of network nodes and a coordinating entity that is operable to coordinate training of the machine learning model at one or more of the plurality of network nodes, the method comprising: (Fig. 4, 7, and 14 [0029]-[0024][0059-[0062] [0122]- [0127] learning of a ML model and a task schedular to schedule to train the ML model at worker nodes) corresponding to the method claim 1 above and, therefore, is rejected for the same reasons set forth in the rejections of claim 1.
the first network node, (Fig. 14, [0059]-[0063] [0122]-[0127] receive the worker node ask the task scheduler to work (train) the ML model)
in response to receiving the machine learning model from the coordinating entity, ([0051]-[0059] [0122]-[0127] the worker pull the updated partial model parameter from the server)
training the machine learning model using training data that is available to the first network node. (Fig. 11B, 14, [0030]-[0033][0050]-[0055] [0059]-[0063] [0114]-[0116] [0120]-[0127], training the ML model with the learning dataset)
In regard to claim 22, Qiu, Johnson, Jeuk disclose A method as claimed in claim17,
Qiu wherein: the training data that is available to the first network node comprises data from one or more devices registered to the first network node. (Fig. 4, [0051]-[0055] the training data is from the learning dataset 416 database which can communicate with)
In regard to claim 25, claim 25 disclose a computer program product claim corresponding to the method claim 1 above and, therefore, is rejected for the same reasons set forth in the rejections of claim 1.
Claims 3, 9, 12-13, 18 are rejected under 35 U.S.C. 103 as being unpatentable over Qiu et al. (Qiu) US 20210133555 and Johnson et al. (Johnson) US 2021/0089887, Jeuk et al. (Jeuk) US 2021/0392049 as applied to claim 1, further in view of Heinrich et al. (Heinrich) US 2020/0226461
In regard to claim 3, Qiu and Johnson, Jeuk disclose A method as claimed in claim 1,
Qiu disclose the method comprising: in response to receiving the trained machine learning model from the first network node: (Fig. 4, [0051]-[0055] server receive the trained ML mode from the worker nodes)
But Qiu and Johnson, Jeuk fail to explicitly disclose “checking whether the trained machine learning model meets a predefined threshold for one or more performance metrics.”
Heinrich disclose checking whether the trained machine learning model meets a predefined threshold for one or more performance metrics. ([0030]-[0037][0053]-[0055] measure performance metrics of the ML model require the performance metrics higher than the quantile threshold)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Heinrich‘s ML training performance metrics into Jeuk, Johnson and Qiu’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Heinrich‘s identifying ML training performance metrics would help to provide model training evaluation method into Jeuk, Johnson and Qiu’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing model training evaluation method by performance metrics would facilitate ML training.
In regard to claim 9, Qiu, Johnson, Jeuk and Heinrich, Walters disclose A method as claimed in claim 3,
Qiu disclose the method comprising: in response to the first network node completing the training of the machine learning model, or in response to a failure of the first network node (10) to train the machine learning model: ([0105]-[-0109][0114]-[0116] the worker complete the training task)
selecting, from the plurality of network nodes, a second network node to further train the trained machine learning model based on information indicative of a performance of each of the plurality of network nodes and/or information indicative of a quality of a network connection to each of the plurality of network nodes, wherein the first network node and the second network node are different network nodes; (Fig. 11B, 14, [0059]-[0063][0103]-[0116] [0120]-[0127], claim 4-5, selecting another worker node (worker 2, for example) form the worker group to training the ML model based on the worker nodes are idle or not performing a ML task)
initiating transmission of a request towards the second network node to trigger a transfer of the trained machine learning model from the first network node to the second network node for the second network node to further train the machine learning model; ([0103]-[0116] communicate the model parameter with each of the other workers with the communication protocols in the worker group and for the other workers to train the model) and
if the trained machine learning model fails to meet the one or more performance metrics, selecting the second network node to further train the trained machine learning model and initiating the transmission of the request towards the second network node to trigger the transfer; or initiating transmission of the trained machine learning model towards an entity that initiated transmission of the request to train the machine learning model. ([0051]-[0059][0095][0096] [0122]-[0127] the worker pushed the trained model parameter to the server which send the transmission request to train the ML model for the worker nodes)
But Qiu and John, Jeuk fail to explicitly disclose “if the trained machine learning model meets the one or more performance metrics,”
Heinrich disclose if the trained machine learning model meets the one or more performance metrics, ([0030]-[0037][0053]-[0055] measure performance metrics of the ML model require the performance metrics higher than the quantile threshold)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Heinrich‘s ML training performance metrics into Jeuk, Johnson and Qiu’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Heinrich‘s identifying ML training performance metrics would help to provide model training evaluation method into Jeuk, Johnson and Qiu’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing model training evaluation method by performance metrics would facilitate ML training.
In regard to claim 12, Qiu, Johnson, Jeuk disclose A method as claimed in claim1,
But Qiu and Johnson, Jeuk fail to explicitly disclose “wherein: the information indicative of the performance of each of the plurality of network nodes comprises: information indicative of an expected performance of each of the plurality of network nodes.”
Heinrich disclose wherein: the information indicative of the performance of each of the plurality of network nodes comprises information indicative of an expected performance of each of the plurality of network nodes. ([0030]-[0041] the information is indicative of a priori information of the expected performance of the nodes)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Heinrich‘s ML training performance metrics into Jeuk, Johnson and Qiu’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Heinrich‘s identifying ML training performance metrics would help to provide model training evaluation method into Jeuk, Johnson and Qiu’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing model training evaluation method by performance metrics would facilitate ML training.
In regard to claim 13, Qiu, Johnson, Jeuk and Heinrich disclose A method as claimed in claim 12,
But Qiu and Johnson, Jeuk fail to explicitly disclose “wherein: the information indicative of the expected performance of each of the plurality of network nodes comprises: a measure of an available compute capacity of each of the plurality of network nodes; and/or a measure of the quality and/or an amount of training data available to each of the plurality of network nodes.”
Heinrich disclose wherein: the information indicative of the expected performance of each of the plurality of network nodes comprises: a measure of an available compute capacity of each of the plurality of network nodes; and/or a measure of the quality and/or an amount of training data available to each of the plurality of network nodes. ([0020] [0030]-[0041] the information is indicative of a priori information of the expected performance of the node, include available computational resources, etc.)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Heinrich‘s ML training performance metrics into Jeuk, Johnson and Qiu’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Heinrich‘s identifying ML training performance metrics would help to provide model training evaluation method into Jeuk, Johnson and Qiu’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing model training evaluation method by performance metrics would facilitate ML training.
In regard to claim 18, Qiu and Johnson, Jeuk disclose A method as claimed in claim 17,
But Qiu and Johnson, Jeuk fail to explicitly disclose “the method performed by the first network node comprising: continuing to train the machine learning model using the training data that is available to the first network node until a maximum accuracy for the trained machine learning model is reached and/or until the first network node runs out of computational capacity to train the machine learning model.”
Heinrich disclose the method performed by the first network node comprising: continuing to train the machine learning model using the training data that is available to the first network node until a maximum accuracy for the trained machine learning model is reached and/or until the first network node runs out of computational capacity to train the machine learning model. ([0020] [0028]-[0041] [0054] train the ML using the training data until the available computational resources is fully used.)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Heinrich‘s ML training performance metrics into Jeuk, Johnson and Qiu’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Heinrich‘s identifying ML training performance metrics would help to provide model training evaluation method into Jeuk, Johnson and Qiu’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing model training evaluation method by performance metrics would facilitate ML training.
Claims 4-7 are rejected under 35 U.S.C. 103 as being unpatentable over Qiu et al. (Qiu) US 20210133555 and Johnson et al. (Johnson) US 2021/0089887, Jeuk et al. (Jeuk) US 2021/039204, Heinrich et al. (Heinrich) US 2020/0226461 as applied to claim 3, further in view of Walters et al. (Walters) US 2020/0012900
In regard to claim 4, Qiu, Johnson, Jeuk and Heinrich disclose A method as claimed in claim 3,
But Qiu and John, Jeuk, Heinrich fail to explicitly disclose “wherein: checking whether the trained machine learning model meets a predefined threshold for one or more performance metrics comprises: comparing an output of the machine learning model resulting from the input of reference data into the machine learning model to an output of the trained machine learning model resulting from an input of the same reference data into the trained machine learning model; and analyzing a difference in the outputs to check whether the trained machine learning model meets the predefined threshold for the one or more performance metrics.”
Walters disclose wherein: checking whether the trained machine learning model meets a predefined threshold for one or more performance metrics comprises:
comparing an output of the machine learning model resulting from the input of reference data into the machine learning model to an output of the trained machine learning model resulting from an input of the same reference data into the trained machine learning model; and analyzing a difference in the outputs to check whether the trained machine learning model meets the predefined threshold for the one or more performance metrics. ([0010]-[0012][0088]-[0092] [0109]-[0110][0128]-[0131] [0162] [0168]-[0174] [0180]-[0198] detect a data drift based on a difference output generated between the trained model from a baseline model parameter from the same dataset and identify the trained model meet or exceeds the threshold of the performance metrics)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Walters‘s detecting data drift in ML training into Jeuk, Heinrich, Johnson and Qiu’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Walters‘s detecting data drift based on the difference of predicted data of the models would help to provide model training evaluation method into Jeuk, Heinrich, Johnson and Qiu’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing model training evaluation method by checking performance metrics would facilitate ML training.
In regard to claim 5, Qiu, Johnson, Jeuk and Heinrich, Walters disclose A method as claimed in claim 4,
Qiu disclose the method comprising: updating a reputation index for the first network node based on the difference in the outputs, wherein the reputation index for the first network node is a measure of the effectiveness of the first network node in training machine learning models compared to other network nodes of the plurality of network nodes. ([0101]-[0113] evaluate the model training effectiveness in the worker node compared to other worker nodes with various values which can be user defined and could use an index to express the values)
In regard to claim 6, Qiu, Johnson, Jeuk and Heinrich, Walters disclose A method as claimed in claim 4,
Qiu, Johnson, Jeuk and Heinrich fail to explicitly disclose “the method comprising: determining whether to add training data, used by the first network node to train the machine learning model, to the reference data based on the difference in the outputs.”
Walters disclose the method comprising: determining whether to add training data, used by the first network node to train the machine learning model, to the reference data based on the difference in the outputs. ([0109]-[0110][0126]-[0133] [0162] [0168]-[0174] [0180]-[0198] add new data to the training model from the reference data based on the difference of the predicted output)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Walters‘s detecting data drift in ML training into Jeuk, Heinrich, Johnson and Qiu’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Walters‘s detecting data drift based on the difference of predicted data of the models would help to provide model training evaluation method into Jeuk, Heinrich, Johnson and Qiu’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing model training evaluation method by checking performance metrics would facilitate ML training.
In regard to claim 7, Qiu, Johnson, Jeuk, and Heinrich, Walters disclose A method as claimed in claim 6,
But Qiu, Johnson, Jeuk and Heinrich fail to explicitly disclose “the method comprising: in response to determining the training data is to be added to the reference data: initiating transmission of a request for the training data towards the first network node; and in response to receiving the training data: adding the training data to the reference data”
Walters disclose the method comprising: in response to determining the training data is to be added to the reference data: [0168]-[0174] [0180]-[0198] determine to add new data to the training model from the reference data based on the difference of the predicted output and add the new data) initiating transmission of a request for the training data towards the first network node; and in response to receiving the training data: ([0048] [0063]-[0068] [0076]-[0079][0109]-[0110] [0127]-[0133] require distribution of the training data to the component of system and in response to receive the training data) adding the training data to the reference data. ([0109]-[0110][0126]-[0133] [0162] [0168]-[0174] [0180]-[0198] determine to add new data to the training model from the reference data and add the new data)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Walters‘s detecting data drift in ML training into Jeuk, Heinrich, Johnson and Qiu’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Walters‘s detecting data drift based on the difference of predicted data of the models would help to provide model training evaluation method into Jeuk, Heinrich, Johnson and Qiu’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing model training evaluation method by checking performance metrics would facilitate ML training.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Qiu et al. (Qiu) US 20210133555 and Johnson et al. (Johnson) US 2021/0089887, Jeuk et al. (Jeuk) US 2021/039204 as applied to claim 1, further in view of Hagdahl et al. (Hagdahl) US 11030134
In regard to claim 14, Qiu, Johnso, Jeuk disclose A method as claimed in claim1,
But Qiu, Johnson and Jeuk fail to explicitly disclose “wherein: the information indicative of the quality of the network connection to each of the plurality of network node comprises: a measure of an available throughput of the network connection to each of the plurality of network nodes; a measure of a latency of the network connection to each of the plurality of network nodes; and/or a measure of a reliability of the network connection to each of the plurality of network.”
Hagdahl disclose wherein: the information indicative of the quality of the network connection to each of the plurality of network node comprises: a measure of an available throughput of the network connection to each of the plurality of network nodes; a measure of a latency of the network connection to each of the plurality of network nodes; and/or a measure of a reliability of the network connection to each of the plurality of network. (col. 8, line 22-col. 9, line 21, the performance metric is QOS of the connection with latency, throughput, etc.)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Hagdahl‘s performance metrics into Jeuk, Johnson and Qiu’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Heinrich‘s identifying performance metrics would help to provide model training evaluation method into Jeuk, Johnson and Qiu’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing model training evaluation method by performance metrics would facilitate ML training.
Claims 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Qiu et al. (Qiu) US 20210133555 and Johnson et al. (Johnson) US 2021/0089887, Jeuk et al. (Jeuk) US 2021/039204 as applied to claim 1, further in view of Healy et al. (Healy) US 20210406369
In regard to claim 19, Qiu, Johnson, Jeuk disclose A method as claimed in claim 17,
But Qiu and Johnson, Jeuk fail to explicitly disclose “the method performed by the first network node comprising: in response to receiving a request for the training data, wherein transmission of the request is initiated by the coordinating entity, initiating transmission of the training data towards the coordinating entity.”
Healy disclose the method performed by the first network node comprising: in response to receiving a request for the training data, wherein transmission of the request is initiated by the coordinating entity, initiating transmission of the training data towards the coordinating entity. ([0025]-[0033] transmit the training data from the device to the server in response to a request through a network interface at the server)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Healy‘s AI models into Jeuk, Johnson and Qiu’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Healy‘s method of requesting training data would help to provide training data gathering method into Jeuk, Johnson and Qiu’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing training data gathering method would facilitate ML training.
In regard to claim 20, Qiu, Johnson, Jeuk disclose A method as claimed in claim17,
But Qiu and Johnson, Jeuk fail to explicitly disclose “the method performed by the first network node comprising: initiating transmission of the trained machine learning model towards the coordinating entity.”
Healy disclose the method performed by the first network node comprising: initiating transmission of the trained machine learning model towards the coordinating entity. ([0025]-[0033] transmit the training data from the device to the server in response to a request through a network interface at the server)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Healy‘s AI models into Jeuk, Johnson and Qiu’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Healy‘s method of requesting training data would help to provide training data gathering method into Jeuk, Johnson and Qiu’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing training data gathering method would facilitate ML training.
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Qiu et al. (Qiu) US 20210133555 and Johnson et al. (Johnson) US 2021/0089887, Jeuk et al. (Jeuk) US 2021/039204 as applied to claim 1, further in view of Kim et al. (KIm) US 20210374503
In regard to claim 21, Qiu, Johnson, Jeuk disclose A method as claimed in claim17,
But Qiu and Johnson, Jeuk fail to explicitly disclose “the method performed by the first network node comprising: in response to receiving a request to trigger a transfer of the trained machine learning model from the first network node to the second network node: initiating the transfer of the trained machine learning model from the first network node to the second network node for the second network node to further train the machine learning model.
Kim disclose the method performed by the first network node comprising: in response to receiving a request to trigger a transfer of the trained machine learning model from the first network node to the second network node: initiating the transfer of the trained machine learning model from the first network node to the second network node for the second network node to further train the machine learning model. ([0030][0043]-[0048] in response to a request to transfer the model, transfer the model from the first node to the second node to make the second node to train the model with TCP/IP communication protocol which include the request and response communications)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Kim’s distributed training of neural networks into Jeuk, Johnson and Qiu’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Kim’s distributed training of neural networks would help to provide training models between the nodes into Jeuk, Johnson and Qiu’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing training models between the nodes would facilitate ML training.
Response to Arguments
Applicant’s arguments with respect to claims 1-22, 24-25 filed on 3/18/2026 have been considered but are moot because the arguments do not apply to the current rejection.
With respect to 35 USC § 101 rejection, please see rejection above for detail.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
U.S. Patent Documents PATENT DATE INVENTOR(S) TITLE
US 20150135012 A1 2015-05-14 Bhalla et al.
NETWORK NODE FAILURE PREDICTIVE SYSTEM
Bhalla et al. disclose In an example, network node failures may be predicted by extracting performance metrics for the network nodes from a plurality of data sources. A fail condition may be defined for the network nodes and input variables related to the fail condition for the network nodes may then be derived from the extracted performance metrics. A plurality of models may then be trained to predict the fail condition for the network nodes using a training set from the extracted performance metrics with at least one of the identified input variables. Each of the plurality of trained models may be validated using a validation set from the extracted performance metrics and may be rated according to predefined criteria. As a result, a highest rated model of the validated models may be selected to predict the fail condition for the network nodes…. see abstract.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to XUYANG XIA whose telephone number is (571)270-3045. The examiner can normally be reached Monday-Friday 8am-4pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Welch can be reached at 571-272-7212. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
XUYANG XIA
Primary Examiner
Art Unit 2143
/XUYANG XIA/Primary Examiner, Art Unit 2143
Prosecution Insights